I think the deeper awesomeness is that DNNs so good at Feature Learning from raw data. On vision, NLP, and speech problems [nice overview by Andrew Ng: https://m.youtube.com/watch?v=W15K9PegQt0] DNNs have achieved superior performance to the combination of expertly-engineered features + some usual ML algorithm.
Where a "usual ML" pipeline might look like (1) engineer features through manual effort by studying raw data and the problem domain, (2) apply ML to those features, a new DNN pipeline might look like (1) Apply DNN to raw data.
First off, removing the feature engineering step could be a huge savings in human time spent. Second, there's the potential to get a better answer (!) when you're done.
But more than that, the DNN pipeline holds the promise of more regular, systematic improvement. We (as engineers) don't have to wait for a bright idea about how to construct a feature from the data. Instead, we can focus on (1) collecting more and better data, (2) improving the optimization algorithms, and (3 acquiring more
computing resources.
These latter tasks, I suspect, are easier to define and evaluate than the task "discover a new feature".
Yep. Have a look at DNNs for image recognition, or LSTM RNN. They're the results of some furious architectural work by researchers and not at all simple to come up with (though they may be simple enough to understand now someone's created them).
There's pure performance (ex., in a Kaggle competition [http://blog.kaggle.com/2012/11/01/deep-learning-how-i-did-it...] or on a standard data set [http://yann.lecun.com/exdb/mnist/], [http://blogs.microsoft.com/next/2015/12/10/microsoft-researc...] ), but that's what makes any ML method better than another.
I think the deeper awesomeness is that DNNs so good at Feature Learning from raw data. On vision, NLP, and speech problems [nice overview by Andrew Ng: https://m.youtube.com/watch?v=W15K9PegQt0] DNNs have achieved superior performance to the combination of expertly-engineered features + some usual ML algorithm.
Where a "usual ML" pipeline might look like (1) engineer features through manual effort by studying raw data and the problem domain, (2) apply ML to those features, a new DNN pipeline might look like (1) Apply DNN to raw data.
First off, removing the feature engineering step could be a huge savings in human time spent. Second, there's the potential to get a better answer (!) when you're done.
But more than that, the DNN pipeline holds the promise of more regular, systematic improvement. We (as engineers) don't have to wait for a bright idea about how to construct a feature from the data. Instead, we can focus on (1) collecting more and better data, (2) improving the optimization algorithms, and (3 acquiring more computing resources.
These latter tasks, I suspect, are easier to define and evaluate than the task "discover a new feature".